@inproceedings{10.1007/978-3-030-39688-6_40,

title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},

editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},

url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_40, Springer },

doi = {10.1007/978-3-030-39688-6_40},

isbn = {978-3-030-39688-6},

year  = {2020},

date = {2020-01-30},

booktitle = {ICTE in Transportation and Logistics 2019},

pages = {319--327},

publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},

address = {Cham},

abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},

keywords = {Human Factors, Human-computer interaction (HCI), Mobility, personalization, Process Support, Process-Mining, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2014e,

title = {User Similarity and Deviation Analysis for Adaptive Visualizations},

author = {Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer and Arjan Kuijper},

editor = {Sakae Yamamoto},

url = {https://link.springer.com/chapter/10.1007/978-3-319-07731-4_7, Springer link},

doi = {10.1007/978-3-319-07731-4_7},

isbn = {978-3-319-07731-7},

year  = {2014},

date = {2014-08-01},

booktitle = {International Conference on Human Interface and the Management of Information (HMI 2014). Human Interface and the Management of Information. Information and Knowledge Design and Evaluation.},

pages = {64--75},

publisher = {Springer International Publishing },

address = {Cham},

series = {LNCS 8521},

abstract = {Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, reference model, Semantic visualization, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2014b,

title = {Adaptive Visualization of Linked-Data},

author = {Kawa Nazemi and Dirk Burkhardt and Reimond Retz and Arjan Kuijper and Jörn Kohlhammer},

editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},

url = {https://link.springer.com/chapter/10.1007/978-3-319-14364-4_84, Springer link},

doi = {10.1007/978-3-319-14364-4_84},

isbn = {978-3-319-14364-4},

year  = {2014},

date = {2014-03-01},

booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},

pages = {872--883},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 8888},

abstract = {Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2014,

title = {Adaptive Visualization of Social Media Data for Policy Modeling},

author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer},

editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},

url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link},

doi = {10.1007/978-3-319-14249-4_32},

isbn = {978-3-319-14249-4},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},

pages = {333--344},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 8887},

abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi:2014:MCR:2637748.2638416,

title = {Measuring Context Relevance for Adaptive Semantics Visualizations},

author = {Kawa Nazemi and Arjan Kuijper and Marco Hutter and Jörn Kohlhammer and Dieter W Fellner},

url = {https://doi.acm.org/10.1145/2637748.2638416, ACM DL},

doi = {10.1145/2637748.2638416},

isbn = {978-1-4503-2769-5},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business},

pages = {14:1--14:8},

publisher = {ACM},

address = {Graz, Austria},

series = {i-KNOW '14},

abstract = {Semantics visualizations enable the acquisition of information to amplify the acquisition of knowledge. The dramatic increase of semantics in form of Linked Data and Linked-Open Data yield search databases that allow to visualize the entire context of search results. The visualization of this semantic context enables one to gather more information at once, but the complex structures may as well confuse and frustrate users. To overcome the problems, adaptive visualizations already provide some useful methods to adapt the visualization on users' demands and skills. Although these methods are very promising, these systems do not investigate the relevance of semantic neighboring entities that commonly build most information value. We introduce two new measurements for the relevance of neighboring entities: The Inverse Instance Frequency allows weighting the relevance of semantic concepts based on the number of their instances. The Direct Relation Frequency inverse Relations Frequency measures the relevance of neighboring instances by the type of semantic relations. Both measurements provide a weighting of neighboring entities of a selected semantic instance, and enable an adaptation of retinal variables for the visualized graph. The algorithms can easily be integrated into adaptive visualizations and enhance them with the relevance measurement of neighboring semantic entities. We give a detailed description of the algorithms to enable a replication for the adaptive and semantics visualization community. With our method, one can now easily derive the relevance of neighboring semantic entities of selected instances, and thus gain more information at once, without confusing and frustrating users.},

note = {Honourable Mention},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, data weighting, Information retrieval, semantic processing, Semantic web, Semantics visualization, User Interface, User modeling, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2014f,

title = {Visualization Adaptation Based on Environmental Influencing Factors},

author = {Dirk Burkhardt and Kawa Nazemi and Jose Daniel Encarnacao and Wilhelm Retz and Jörn Kohlhammer},

editor = {Masaaki Kurosu},

url = {https://link.springer.com/chapter/10.1007/978-3-319-07233-3_38, Springer link},

isbn = {978-3-319-07233-3},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Human-Computer (HCI 2014). Human-Computer Interaction. Theories, Methods, and Tools.},

pages = {411--422},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 8510},

abstract = {Working effectively with computer-based devices is challenging, especially under mobile conditions, due to the various environmental influences. In this paper a visualization adaptation approach is described, to support the user under discriminatory environmental conditions. For this purpose, a context model for environmental influencing factors is being defined. Based on this context model, an approach to adapt visualizations in regards of certain environmental influences is being evolved, such as the light intensity, air quality, or heavy vibrations.},

keywords = {Adaptive information visualization, Adaptive user interfaces, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2011d,

title = {Modeling Users for Adaptive Semantics Visualizations},

author = {Kawa Nazemi and Dirk Burkhardt and Matthias Breyer and Arjan Kuijper},

editor = {Constantine Stephanidis},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21663-3_10, Springer link},

doi = {10.1007/978-3-642-21663-3_10},

isbn = {978-3-642-21663-3},

year  = {2011},

date = {2011-01-01},

booktitle = {International Conference on Universal Access in Human-Computer Interaction. Universal Access in Human-Computer Interaction. Users Diversity. },

volume = {2},

pages = {88--97},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6766},

abstract = {The automatic adaptation of information visualization systems to the requirements of users plays a key-role in today's research. Different approaches from both disciplines try to face this phenomenon. The modeling of user is an essential part of a user-centered adaptation of visualization. In this paper we introduce a new approach for modeling users especially for semantic visualization systems. The approach consists of a three dimensional model, where semantic data, user and visualization are set in relation in different abstraction layer.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Intelligent Systems, Interaction analysis, Interaction Design, User modeling},

pubstate = {published},

tppubtype = {inproceedings}

}